Data Merge

Load libraries

# install.packages("plotly")
# install.packages("latex2exp")
# install.packages("BiocManager") 
# BiocManager::install("EBImage")
library(EBImage)

Attaching package: ‘EBImage’

The following object is masked from ‘package:purrr’:

    transpose

library(plotly)

Attaching package: ‘plotly’

The following object is masked from ‘package:EBImage’:

    toRGB

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

library(ggplot2)
library(stringr)
library(latex2exp)

Attaching package: ‘latex2exp’

The following object is masked from ‘package:plotly’:

    TeX

packageVersion('plotly')

[1] ‘4.9.1’

Sys.setenv("plotly_username"="thuynh32")
Sys.setenv("plotly_api_key"="xcSv1yzujDc1IGEwQlr2")

Load data

# persons = c("01", "04", "05", "06", "07", "08", "10", "11", "12")
persons = c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10",
            "12", "13", "15", "16", "17", "18",
            "22", "23", "24", "26", "28", "29", 
            "30", "31", "32", "41")

datas = vector(mode="list", length=length(persons))
datas_baseline = vector(mode="list", length=length(persons))
pp_means = vector(mode="list", length=length(persons))

names(datas) <- persons
names(datas_baseline) <- persons
names(pp_means) <- persons

for (p in persons) {
  datas[[p]] <- read.csv(str_interp("../../../data/TT1/preprocessed/T0${person}/T0${person}_Drive_4.csv", list(person=p)))
  datas_baseline[[p]] <- read.csv(str_interp("../../../data/TT1/preprocessed/T0${person}/T0${person}_Drive_1.csv", list(person=p)))
  
  # Compute the mean
  p_pp_nr <- datas_baseline[[p]]$Perspiration
  p_pp_nr <- p_pp_nr[!is.na(p_pp_nr)]
  pp_means[[p]] <- mean(p_pp_nr)
}

Merge PP Data

all <- data.frame()
all_baseline <- data.frame()
for (p in persons) {
  # On-road
  df_p <- datas[[p]]
  df_p$ppNormalized <- df_p$Perspiration - pp_means[[p]]
  df_p$ppLogNormalized <- log(df_p$Perspiration) - log(pp_means[[p]])
  df_p$Subject <- p
  
  all <- rbind(all, df_p)
  
  # Baseline
  df_p_baseline <- datas_baseline[[p]]
  df_p_baseline$Subject <- p
  all_baseline <- rbind(all_baseline, df_p_baseline)
}
print(length(all$Time))

[1] 2162

print(length(all_baseline$Time))

[1] 12454

Prepare the PP plots

vline <- function(x = 0, color = "red") {
  list(
    type = "line", 
    y0 = 0, 
    y1 = 1, 
    yref = "paper",
    x0 = x, 
    x1 = x, 
    line = list(color = color, dash="dot", width=1)
  )
}
hline <- function(y = 0, color = "blue") {
  list(
    type = "line", 
    x0 = 0, 
    x1 = 1, 
    xref = "paper",
    y0 = y, 
    y1 = y, 
    line = list(color = color, dash="dot", width=1)
  )
}

Draw all PP

plot_all_PP <- plot_ly(all, x = ~Time, y = ~Perspiration, name = ~Subject, 
                       type = 'scatter', mode = 'lines', width = 800, line=list(width=1.5),
                       color = ~Subject) %>% layout(title="", xaxis=list(title="Time [s]"), yaxis=list(title="Perspiration"))
htmltools::tagList(plot_all_PP)

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Draw all PP (Normalized with baseline mean)

plot_all_PP <- plot_ly(all, x = ~Time, y = ~ppNormalized, name = ~Subject, 
                       type = 'scatter', mode = 'lines', width = 800, line=list(width=1.5),
                       color = ~Subject) %>% layout(title="", xaxis=list(title="Time [s]"), yaxis=list(title="Normalized Perspiration"))
htmltools::tagList(plot_all_PP)

n too large, allowed maximum for palette Set2 is 8
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Draw all Speed

plot_all_Speed <- plot_ly(all, x = ~Time, y = ~Speed, name = ~Subject, 
                       type = 'scatter', mode = 'lines', width = 800, line=list(width=1.5),
                       color = ~Subject) %>% layout(title="", xaxis=list(title="Time [s]"), yaxis=list(title="Speed"))
htmltools::tagList(plot_all_Speed)

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Draw all BR

# Because the BR affects to the PP
# What we wanna see here is if the Subject 01 is having higher BR than normal
mean_BR_All <- mean(all$BR)

argument is not numeric or logical: returning NA

mean_BR_P1 <- mean(all[all$Subject=='01',]$BR)

argument is not numeric or logical: returning NA

ant_All <- list(y = mean_BR_All, x = 100, text = "BR mean of all", color="blue")
plot_all_BR <- plot_ly(all, x = ~Time, y = ~Braking, name = ~Subject, 
                       type = 'scatter', mode = 'lines', width = 800, line=list(width=1.5),
                       color = ~Subject) %>% 
  layout(title="", xaxis=list(title="Time [s]"), yaxis=list(title="Breath Rate [BPM]"), 
         shapes=list(hline(mean_BR_All, color="blue")), 
         annotations=list(ant_All))
htmltools::tagList(plot_all_BR)

n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
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n too large, allowed maximum for palette Set2 is 8
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Analyze Density

Configuration

EXP_COLORS <- c("#00AEDB", "#D11141")
QC_COLORS <- c("#00AEDB", "#D11141")

PP

data_PP <- all$Perspiration
data_PP <- data_PP[!is.na(data_PP) & data_PP >= 0.001 & data_PP <= 0.016]
data_PP_Baseline <- all_baseline$Perspiration
data_PP_Baseline <- data_PP_Baseline[!is.na(data_PP_Baseline) & data_PP_Baseline >= 0.001 & data_PP_Baseline <= 0.016]

mean_PP <- mean(data_PP)
mean_PP_Baseline <- mean(data_PP_Baseline)

df_PP <- data.frame(PP=data_PP)
df_PP_Baseline <- data.frame(PP=data_PP_Baseline)
df_PP$Experiment <- "On-Road Driving"
df_PP_Baseline$Experiment <- "Baseline"

df_PP_All <- rbind(df_PP, df_PP_Baseline)

plot_PP <- ggplot(df_PP_All, aes(PP, fill=Experiment)) + 
  geom_density(alpha = 0.3) + 
  labs(x = "Perspiration", y = "PDF") + 
  geom_vline(xintercept=mean_PP, linetype="dashed", color=EXP_COLORS[2]) + 
  geom_vline(xintercept=mean_PP_Baseline, linetype="dashed", color=EXP_COLORS[1]) + 
  annotate(geom="text", x=mean_PP_Baseline - 0.00015, y=600, label=paste0("Baseline Mean = ", round(mean_PP_Baseline, 4)), color="blue", angle=90, size=3) +
  annotate(geom="text", x=mean_PP - 0.00015, y=600, label=paste0("ORD Mean = ", round(mean_PP, 4)), color="red", angle=90, size=3, vjust=0) +
  scale_fill_manual(values=EXP_COLORS)
# plot_PP <- ggplotly(plot_PP)
# htmltools::tagList(plot_PP)
print(plot_PP)

PP - Baseline

data_PP_Norm <- all$ppNormalized
data_PP_Norm <- data_PP_Norm[!is.na(data_PP_Norm) & data_PP_Norm >= -0.016 & data_PP_Norm <= 0.016]

mean_PP_Norm <- mean(data_PP_Norm)
threshold_PP_Norm <- mean(data_PP_Norm)

mat_PP_Norm <- matrix(data_PP_Norm,nrow = 1,ncol = length(data_PP_Norm))
threshold_PP_Norm <- otsu(mat_PP_Norm, range=c(min(data_PP_Norm), max(data_PP_Norm)))

df_PP_Norm <- data.frame(PP=data_PP_Norm)
df_PP_Norm$Experiment <- "On-Road Driving"

plot_PP_Norm <- ggplot(df_PP_Norm, aes(PP, fill=Experiment)) + 
  geom_density(alpha = 0.3) + 
  labs(x = TeX("$PP_{OnRoad} - PP_{Baseline}$"), y = "PDF") + 
  theme(legend.position = "none") + 
  geom_vline(xintercept=mean_PP_Norm, linetype="dashed", color="blue") + 
  geom_vline(xintercept=0, linetype="dashed", color="darkgray") + 
  geom_vline(xintercept=threshold_PP_Norm, linetype="dashed", color="red") + 
  annotate(geom="text", x=mean_PP_Norm - 0.00015, y=550, label=paste0("Mean = ", round(mean_PP_Norm, 4)), color="blue", angle=90, size=3) +
  annotate(geom="text", x=0 - 0.00015, y=50, label="Baseline", color="black", angle=90, size=3, vjust=0) +
  annotate(geom="text", x=threshold_PP_Norm - 0.00015, y=550, label=paste0("Threshold = ", round(threshold_PP_Norm, 4)), color="red", angle=90, size=3, vjust=0) +
  scale_fill_manual(values=rev(EXP_COLORS))
#plot_PP_Norm <- ggplotly(plot_PP_Norm)
#htmltools::tagList(plot_PP_Norm)
print(plot_PP_Norm)

log(PP - Baseline)

data_PP_Norm <- all$ppLogNormalized
data_PP_Norm <- data_PP_Norm[!is.na(data_PP_Norm)]

mean_PP_Norm <- mean(data_PP_Norm)
threshold_PP_Norm <- mean(data_PP_Norm)

mat_PP_Norm <- matrix(data_PP_Norm,nrow = 1,ncol = length(data_PP_Norm))
threshold_PP_Norm <- otsu(mat_PP_Norm, range=c(min(data_PP_Norm), max(data_PP_Norm)))

df_PP_Norm <- data.frame(PP=data_PP_Norm)
df_PP_Norm$Experiment <- "On-Road Driving"

plot_PP_Norm <- ggplot(df_PP_Norm, aes(PP, fill=Experiment)) + 
  geom_density(alpha = 0.3) + 
  labs(x = TeX("All Subjects: $\\textit{log}(PP_{OnRoad}) - \\textit{log}(PP_{Baseline})$"), y = "PDF") + 
  theme(legend.position = "none") + 
  geom_vline(xintercept=mean_PP_Norm, linetype="dashed", color="blue") + 
  geom_vline(xintercept=0, linetype="dashed", color="darkgray") + 
  geom_vline(xintercept=threshold_PP_Norm, linetype="dashed", color="red") + 
  annotate(geom="text", x=mean_PP_Norm - 0.025, y=2, label=paste0("Mean = ", round(mean_PP_Norm, 4)), color="blue", angle=90, size=3) +
  annotate(geom="text", x=0 - 0.015, y=2, label="Baseline", color="black", angle=90, size=3, vjust=0) +
  annotate(geom="text", x=threshold_PP_Norm - 0.015, y=2, label=paste0("Threshold = ", round(threshold_PP_Norm, 4)), color="red", angle=90, size=3, vjust=0) +
  scale_fill_manual(values=rev(EXP_COLORS))
#plot_PP_Norm <- ggplotly(plot_PP_Norm)
#htmltools::tagList(plot_PP_Norm)
print(plot_PP_Norm)

Heart Rate

data_HR <- all$HR
data_HR <- data_HR[!is.na(data_HR)]
data_HR_QC1 <- data_HR[(data_HR >= 40) & (data_HR <= 140)]

mean_HR <- mean(data_HR)

argument is not numeric or logical: returning NA

mean_HR_QC1 <- mean(data_HR_QC1)

argument is not numeric or logical: returning NA

df_HR <- data.frame(HR=data_HR)
df_HR_QC1 <- data.frame(HR=data_HR_QC1)
df_HR$Type <- "Before QC1"

Error in `$<-.data.frame`(`*tmp*`, Type, value = "Before QC1") : 
  replacement has 1 row, data has 0

Breath Rate

data_BR <- all$BR
data_BR <- data_BR[!is.na(data_BR)]
data_BR_QC1 <- data_BR[data_BR >= 4 & data_BR <= 40]

mean_BR <- mean(data_BR)

argument is not numeric or logical: returning NA

mean_BR_QC1 <- mean(data_BR_QC1)

argument is not numeric or logical: returning NA

df_BR <- data.frame(BR=data_BR)
df_BR_QC1 <- data.frame(BR=data_BR_QC1)
df_BR$Type <- "Before QC1"

Error in `$<-.data.frame`(`*tmp*`, Type, value = "Before QC1") : 
  replacement has 1 row, data has 0

Brake

data_Brake <- all$Braking
data_Brake <- data_Brake[!is.na(data_Brake)]

mean_Brake <- mean(data_Brake)

df_Brake <- data.frame(Brake=data_Brake)
df_Brake$Type <- "Brake [%]"

mat_Brake <- matrix(data_Brake,nrow = 1,ncol = length(data_Brake))
threshold_Brake <- otsu(mat_Brake, range=c(min(data_Brake), max(data_Brake)))

plot_Brake <- ggplot(df_Brake, aes(Brake, fill=Type)) + 
  geom_density(alpha = 0.3) + 
  labs(x = "Brake [%]", y = "PDF") + 
  geom_vline(xintercept=mean_Brake, linetype="dashed", color="blue") +  
  geom_vline(xintercept=threshold_Brake, linetype="dashed", color="red") + 
  annotate(geom="text", x=threshold_Brake - 1, y=0.05, label=paste0("Threshold = ", round(threshold_Brake, 2)), color="red", angle=90, size=3, vjust=0) + 
  annotate(geom="text", x=mean_Brake - 1, y=0.05, label=paste0("Mean = ", round(mean_Brake, 2)), color="blue", angle=90, size=3, vjust=0) + 
  scale_fill_manual(values=rev(QC_COLORS))
# plot_Brake <- ggplotly(plot_Brake)
# htmltools::tagList(plot_Brake)
print(plot_Brake)

Speed

data_Speed <- all$Speed
data_Speed <- data_Speed[!is.na(data_Speed)]

mean_Speed <- mean(data_Speed)

df_Speed <- data.frame(Speed=data_Speed)
df_Speed$Type <- "Speed [km/h]"

mat_Speed <- matrix(data_Speed,nrow = 1,ncol = length(data_Speed))
threshold_Speed <- otsu(mat_Speed, range=c(min(data_Speed), max(data_Speed)))
adj_Threshold_Speed <- 15

plot_Speed <- ggplot(df_Speed, aes(Speed, fill=Type)) + 
  geom_density(alpha = 0.3) + 
  labs(x = "Speed [km/h]", y = "PDF") + 
  geom_vline(xintercept=mean_Speed, linetype="dashed", color="blue") + 
  geom_vline(xintercept=threshold_Speed, linetype="dashed", color="red") + 
  geom_vline(xintercept=adj_Threshold_Speed, linetype="dashed", color="purple") + 
  annotate(geom="text", x=threshold_Speed - 2, y=0.02, label=paste0("Threshold = ", round(threshold_Speed, 2)), color="red", angle=90, size=3) + 
  annotate(geom="text", x=adj_Threshold_Speed - 2, y=0.02, label=paste0("Adj. Threshold = ", round(adj_Threshold_Speed, 2)), color="purple", angle=90, size=3) + 
  annotate(geom="text", x=mean_Speed + 2, y=0.02, label=paste0("Mean = ", round(mean_Speed, 2)), color="blue", angle=90, size=3) + 
  scale_fill_manual(values=rev(QC_COLORS))
# plot_Speed <- ggplotly(plot_Speed)
# htmltools::tagList(plot_Speed)

print(plot_Speed)

Accelerator

data_Accelerator <- all$Acceleration
data_Accelerator <- data_Accelerator[!is.na(data_Accelerator)]

mean_Accelerator <- mean(data_Accelerator)

df_Accelerator <- data.frame(Accelerator=data_Accelerator)
df_Accelerator$Type <- "Acceleration [%]"

mat_Accelerator <- matrix(data_Accelerator,nrow = 1,ncol = length(data_Accelerator))
threshold_Accelerator <- 8.5 # otsu(mat_Accelerator, range=c(min(data_Accelerator), max(data_Accelerator)))

plot_Accelerator <- ggplot(df_Accelerator, aes(Accelerator, fill=Type)) + 
  geom_density(alpha = 0.3) + 
  labs(x = "Accelerator [%]", y = "PDF") + 
  geom_vline(xintercept=mean_Accelerator, linetype="dashed", color="blue") + 
  geom_vline(xintercept=threshold_Accelerator, linetype="dashed", color="red") + 
  annotate(geom="text", x=mean_Accelerator - 0.5, y=0.05, label=paste0("Mean = ", round(mean_Accelerator, 2)), color="blue", angle=90, size=3, vjust=0) + 
  annotate(geom="text", x=threshold_Accelerator - 0.5, y=0.05, label=paste0("Threshold = ", round(threshold_Accelerator, 2)), color="red", angle=90, size=3, vjust=0) + 
  scale_fill_manual(values=rev(QC_COLORS))

# plot_Accelerator <- ggplotly(plot_Accelerator)
# htmltools::tagList(plot_Accelerator)
print(plot_Accelerator)

Steering

data_Steering <- all$Steering
data_Steering <- data_Steering[!is.na(data_Steering) & abs(data_Steering) < 500]
data_Steering_QC1 <- data_Steering[abs(data_Steering) <= 360]

mean_Steering <- mean(data_Steering)
mean_Steering_QC1 <- mean(data_Steering_QC1)

df_Steering <- data.frame(Steering=data_Steering)
df_Steering_QC1 <- data.frame(Steering=data_Steering_QC1)
df_Steering$Type <- "Before QC1"
df_Steering_QC1$Type <- "After QC1"

df_Steering_All <- rbind(df_Steering, df_Steering_QC1)

plot_Steering <- ggplot(df_Steering_All, aes(Steering, fill=Type)) + 
  geom_density(alpha = 0.3) + 
  labs(x = "Steering [Degree]", y = "PDF") + 
  geom_vline(xintercept=mean_Steering, linetype="dashed", color=QC_COLORS[2]) + 
  geom_vline(xintercept=mean_Steering_QC1, linetype="dashed", color=QC_COLORS[1]) + 
  scale_fill_manual(values=QC_COLORS)
# plot_Steering <- ggplotly(plot_Steering)
# htmltools::tagList(plot_Steering)

print(plot_Steering)

Map

Sys.setenv('MAPBOX_TOKEN' = 'pk.eyJ1IjoidGh1eW5oMzIiLCJhIjoiY2p6endwbXY3MjJ0cTNtbWwzN3E0enZuciJ9.fpzzkDjWHb0LOcTog8B6Lg')
MAP_MODE_COLORS <- c("#7c91f5", "#ea4335")

all$IN_Lat_Degree <- all$IN_Lat / 60
all$IN_Long_Degree <- all$IN_Long / 60
mapCenter <- list(lat = mean(all$IN_Lat_Degree), lon = mean(all$IN_Long_Degree))

Map of Speed

all$cls_Speed <- ifelse(all$Speed > threshold_Speed, "High Speed", "Low Speed")
all$diff_Speed <- abs(all$Speed - threshold_Speed)
pMap_Speed <- all %>% plot_mapbox(lat=~IN_Lat_Degree, lon=~IN_Long_Degree, split=~cls_Speed, size=~diff_Speed, mode = 'scattermapbox', hoverinfo='text', text=~Subject, alpha = 1, opacity=0.1, height=600) %>%
              layout(title = 'Speeding spots',
                     legend = list(orientation = 'h', font = list(size = 8)),
                     colorway=rev(MAP_MODE_COLORS),
                     mapbox = list(zoom = 12, center = mapCenter))
htmltools::tagList(pMap_Speed)

Map of Accelerator

all$cls_Accel <- ifelse(all$Accelerator > threshold_Accelerator, "High Acceleration", "Low Acceleration")
pMap_Accel <- all %>% plot_mapbox(lat=~IN_Lat_Degree, lon=~IN_Long_Degree, split=~cls_Accel, size=~Accelerator, height=600,
              mode = 'scattermapbox', hoverinfo='text', text=~Subject, alpha = 1, opacity=0.1) %>%
              layout(title = 'Acceleration spots',
                     legend = list(orientation = 'h', font = list(size = 8)),
                     colorway=rev(MAP_MODE_COLORS),
                     mapbox = list(zoom = 12, center = center))
htmltools::tagList(pMap_Accel)

Map of Brake

all$cls_Brake <- ifelse(all$Brake > threshold_Brake, "High Brake", "Low Brake")
all_Brake <- all[order(all$cls_Brake, decreasing = F),]
pMap_Brake <- all_Brake %>% plot_mapbox(lat=~IN_Lat_Degree, lon=~IN_Long_Degree, split=~cls_Brake, size=~Brake, height=600,
              mode = 'scattermapbox', hoverinfo='text', text=~Subject, alpha = 1, opacity=0.1) %>%
              layout(title = 'Brake spots',
                     legend = list(orientation = 'h', font = list(size = 8)),
                     colorway=rev(MAP_MODE_COLORS),
                     mapbox = list(zoom = 12, center = center))
htmltools::tagList(pMap_Brake)

Map of PP

all$cls_PP <- ifelse(!is.na(all$ppNormalized) & all$ppNormalized >= threshold_PP_Norm, "High PP", "Low PP")
all_PP <- all[order(all$cls_PP, decreasing = F),]
pMap_PP <- all_PP %>% plot_mapbox(lat=~IN_Lat_Degree, lon=~IN_Long_Degree, split=~cls_PP, size=~ppNormalized, height=600,
              mode = 'scattermapbox', hoverinfo='text', text=~paste(Subject, ": ", ppNormalized), alpha = 1, opacity=0.1) %>%
              layout(title = 'PP spots',
                     legend = list(orientation = 'h', font = list(size = 8)),
                     colorway=rev(MAP_MODE_COLORS),
                     mapbox = list(zoom = 12, center = center))
htmltools::tagList(pMap_PP)